Ad Reporting dbt Package (Docs)

What does this dbt package do?

• Standardizes schemas from various ad platform connectors and creates reporting models for clicks, spend and impressions aggregated to the account, campaign, ad group, ad, keyword and search levels.
• Currently supports the following Fivetran ad platform connectors:
  • Amazon Ads
  • Apple Search Ads
  • Facebook Ads
  • Google Ads
  • LinkedIn Ad Analytics
  • Microsoft Advertising
  • Pinterest Ads
  • Reddit Ads
  • Snapchat Ads
  • TikTok Ads
  • Twitter Ads

NOTE: You do not need to have all of these connector types to use this package, though you should have at least two.

• Generates a comprehensive data dictionary of your source and modeled Ad Reporting data via the dbt docs site

Refer to the table below for a detailed view of final tables materialized by default within this package. Additionally, check out our Docs site for more details about these models.

Table	Description
ad_reporting__account_report	Each record represents daily metrics by account
ad_reporting__campaign_report	Each record represents daily metrics by campaign and account.
ad_reporting__ad_group_report	Each record represents daily metrics by ad group, campaign and account.
ad_reporting__ad_report	Each record represents daily metrics by ad, ad group, campaign and account.
ad_reporting__keyword_report	Each record represents daily metrics by keyword, ad group, campaign and account.
ad_reporting__search_report	Each record represents daily metrics by search query, ad group, campaign and account.
ad_reporting__url_report	Each record represents daily metrics by URL (and if applicable, URL UTM parameters), ad group, campaign and account.

The individual platform models may have additional platform-specific metrics and fields better suited for deep-dive analyses at the platform level.

How do I use the dbt package?

Step 1: Pre-Requisites

Connector: Have at least one of the below supported Fivetran ad platform connectors syncing data into your warehouse. This package currently supports: - Amazon Ads - Apple Search Ads - Facebook Ads - Google Ads - LinkedIn Ad Analytics - Microsoft Advertising - Pinterest Ads - Reddit Ads - Snapchat Ads - TikTok Ads - Twitter Ads

While you need only one of the above connectors to utilize this package, we recommend having at least two to gain the rollup benefit of this package.

• Database support: This package has been tested on BigQuery, Snowflake, Redshift, Postgres and Databricks. Ensure you are using one of these supported databases.

Databricks Dispatch Configuration

If you are using a Databricks destination with this package you will need to add the below (or a variation of the below) dispatch configuration within your dbt_project.yml. This is required in order for the package to accurately search for macros within the dbt-labs/spark_utils then the dbt-labs/dbt_utils as well as the calogica/dbt_expectations then the google_ads_source packages respectively.

dispatch:
  - macro_namespace: dbt_utils
    search_order: ['spark_utils', 'dbt_utils']

  - macro_namespace: dbt_expectations
    search_order: ['google_ads_source', 'dbt_expectations']

Step 2: Installing the Package

Include the following github package version in your packages.yml

Check dbt Hub for the latest installation instructions, or read the dbt docs for more information on installing packages.

packages:
  - package: fivetran/ad_reporting
    version: [">=1.10.0", "<1.11.0"] # we recommend using ranges to capture non-breaking changes automatically

Do NOT include the individual ad platform packages in this file. The ad reporting package itself has dependencies on these packages and will install them as well.

Step 3: Configure Database and Schema Variables

By default, this package looks for your ad platform data in your target database. If this is not where your app platform data is stored, add the relevant <connector>_database variables to your dbt_project.yml file (see below).

vars:
    amazon_ads_schema: amazon_ads
    amazon_ads_database: your_database_name

    apple_search_ads_schema: apple_search_ads
    apple_search_ads_database: your_database_name

    facebook_ads_schema: facebook_ads
    facebook_ads_database: your_database_name 

    google_ads_schema: google_ads
    google_ads_database: your_database_name 

    microsoft_ads_schema: bingads
    microsoft_ads_database: your_database_name

    linkedin_ads_schema: linkedin_ads 
    linkedin_ads_database: your_database_name  

    pinterest_schema: pinterest
    pinterest_database: your_database_name 

    reddit_ads_schema: reddit_ads
    reddit_ads_database: your_database_name 

    snapchat_schema: snapchat_ads
    snapchat_database: your_database_name 
    
    tiktok_ads_schema: tiktok_ads
    tiktok_ads_database: your_database_name

    twitter_ads_schema: twitter_ads
    twitter_ads_database: your_database_name  

Step 4: Enabling/Disabling Models

This package takes into consideration that not every account will have every feature enabled per platform. If your syncs exclude certain tables, it is because you either don't use that functionality in your respective ad platforms or have actively excluded some tables from your syncs.

Disable Platform Specific Reporting

If you would like to disable all reporting for any specific platform, please include the respective variable(s) in your dbt_project.yml. Each is assumed to be True by default.

vars:
  ad_reporting__amazon_ads_enabled: False # Amazon Ads package includes 7 end models + 23 staging/intermediate models
  ad_reporting__apple_search_ads_enabled: False # Apple Search Ads package includes 6 end models + 20 staging models
  ad_reporting__facebook_ads_enabled: False # Facebook Ads package includes 6 end models + 18 staging/intermediate models
  ad_reporting__google_ads_enabled: False # Google Ads package includes 6 end models + 20 staging models
  ad_reporting__linkedin_ads_enabled: False # Linkedin Ad Analytics package includes 5 end models + 12 staging models
  ad_reporting__microsoft_ads_enabled: False # Microsoft Advertising package includes 7 end models + 22 staging models
  ad_reporting__pinterest_ads_enabled: False # Pinterest Ads package includes 6 end models + 20 staging models
  ad_reporting__reddit_ads_enabled: False # Reddit Ads package includes 5 end models + 24 staging models
  ad_reporting__snapchat_ads_enabled: False # Snapchat Ads package includes 5 end models + 8 staging models
  ad_reporting__tiktok_ads_enabled: False # TikTok Ads package includes 5 end models + 14 staging models
  ad_reporting__twitter_ads_enabled: False # Twitter Ads package includes 6 end models + 20 staging models

Enable/Disable Specific Reports within Platforms

For Apple Search Ads, if you are not utilizing the search functionality, you may choose to update the respective variable below.

For Pinterest Ads, if you are not tracking keyword performance, you may choose to update the corresponding variable below.

For Twitter Ads, if you are not tracking keyword performance, you may choose to update the corresponding variable below.

Add the following variables to your dbt_project.yml file

vars:
  apple_search_ads__using_search_terms: False # by default this is assumed to be True
  pinterest__using_keywords: False # by default this is assumed to be True
  twitter_ads__using_keywords: False # by default this is assumed to be True

(Recommended) Step 5: Change the Build Schema

By default this package will build all models in your <target_schema> with the respective package suffixes (see below). This behavior can be tailored to your preference by making use of custom schemas. If you would like to override the current naming conventions, please add the following configuration to your dbt_project.yml file and rename +schema configs:

models:  
  ad_reporting:
    +schema: ad_reporting

  amazon_search_ads:
    +schema: amazon_ads
  amazon_ads_source:
    +schema: amazon_ads_source

  apple_search_ads:
    +schema: apple_search_ads
  apple_search_ads_source:
    +schema: apple_search_ads_source
  
  facebook_ads:
    +schema: facebook_ads
  facebook_ads_source:
    +schema: facebook_ads_source
  
  google_ads:
    +schema: google_ads
  google_ads_source:
    +schema: google_ads_source

  linkedin:
    +schema: linkedin
  linkedin_source:
    +schema: linkedin_source

  microsoft_ads:
    +schema: microsoft_ads
  microsoft_ads_source:
    +schema: microsoft_ads_source

  pinterest:
    +schema: pinterest
  pinterest_source:
    +schema: pinterest_source

  reddit_ads:
    +schema: reddit_ads
  reddit_ads_source:
    +schema: reddit_ads_source

  snapchat_ads:
    +schema: snapchat_ads
  snapchat_ads_source:
    +schema: snapchat_ads_source
  
  tiktok_ads:
    +schema: tiktok_ads
  tiktok_ads_source:
    +schema: tiktok_ads_source
  
  twitter_ads:
    +schema: twitter_ads
  twitter_ads_source:
    +schema: twitter_ads_source

Provide a blank +schema: to write to the target_schema without any suffix.

(Optional) Step 6: Additional configurations

Expand/Collapse details

Union multiple connectors

If you have multiple ad reporting connectors in Fivetran and would like to use this package on all of them simultaneously, we have provided functionality to do so. The package will union all of the data together and pass the unioned table into the transformations. You will be able to see which source it came from in the source_relation column of each model. To use this functionality, you will need to set either the <package_name>_union_schemas OR <package_name>_union_databases variables (cannot do both) in your root dbt_project.yml file. Below are the variables and examples for each connector:

vars:
    amazon_ads_union_schemas: ['amazon_ads_usa','amazon_ads_canada']
    amazon_ads_union_databases: ['amazon_ads_usa','amazon_ads_canada']

    apple_search_ads_union_schemas: ['apple_search_ads_usa','apple_search_ads_canada']
    apple_search_ads_union_databases: ['apple_search_ads_usa','apple_search_ads_canada']

    facebook_ads_union_schemas: ['facebook_ads_usa','facebook_ads_canada']
    facebook_ads_union_databases: ['facebook_ads_usa','facebook_ads_canada']

    google_ads_union_schemas: ['google_ads_usa','google_ads_canada']
    google_ads_union_databases: ['google_ads_usa','google_ads_canada']

    linkedin_ads_union_schemas: ['linkedin_usa','linkedin_canada']
    linkedin_ads_union_databases: ['linkedin_usa','linkedin_canada']

    microsoft_ads_union_schemas: ['microsoft_ads_usa','microsoft_ads_canada']
    microsoft_ads_union_databases: ['microsoft_ads_usa','microsoft_ads_canada']

    pinterest_ads_union_schemas: ['pinterest_usa','pinterest_canada']
    pinterest_ads_union_databases: ['pinterest_usa','pinterest_canada']

    reddit_ads_union_schemas: ['reddit_ads_usa','reddit_ads_canada']
    reddit_ads_union_databases: ['reddit_ads_usa','reddit_ads_canada']

    snapchat_ads_union_schemas: ['snapchat_ads_usa','snapchat_ads_canada']
    snapchat_ads_union_databases: ['snapchat_ads_usa','snapchat_ads_canada']

    tiktok_ads_union_schemas: ['tiktok_ads_usa','tiktok_ads_canada']
    tiktok_ads_union_databases: ['tiktok_ads_usa','tiktok_ads_canada']

    twitter_ads_union_schemas: ['twitter_usa','twitter_canada']
    twitter_ads_union_databases: ['twitter_usa','twitter_canada']

NOTE: The native source.yml connection set up in the package will not function when the union schema/database feature is utilized. Although the data will be correctly combined, you will not observe the sources linked to the package models in the Directed Acyclic Graph (DAG). This happens because the package includes only one defined source.yml.

To connect your multiple schema/database sources to the package models, follow the steps outlined in the Union Data Defined Sources Configuration section of the Fivetran Utils documentation for the union_data macro. This will ensure a proper configuration and correct visualization of connections in the DAG.

Configure types of conversion events

For the following platforms, conversion data is sent along with the type of event that you may or may not consider to be a conversion. For the most part, the packages consider leads, purchases, and custom user-defined events as conversions by default, but this can be configured via the appropriate variables.

Platform	Variable	Default Values	How to Use
Facebook Ads	facebook_ads__conversion_action_types	offsite_conversion.fb_pixel_custom + offsite_conversion.fb_pixel_lead + onsite_conversion.lead_grouped + offsite_conversion.fb_pixel_purchase + onsite_conversion.purchase	Configuring Conversion Action Types
LinkedIn Ad Analytics	linkedin_ads__conversion_fields	external_website_conversions + one_click_leads	Adding in Conversion Fields
Reddit Ads	reddit_ads__conversion_event_types	lead + purchase + custom	Configure Conversion Event Types
Snapchat Ads	snapchat_ads__conversion_fields	conversion_purchases	Configuring Conversion Fields
Twitter Ads	twitter_ads__conversion_fields AND twitter_ads__conversion_sale_amount_fields	conversion_purchases_metric + conversion_custom_metric AND conversion_purchases_sale_amount + conversion_custom_sale_amount	Customizing Types of Conversions

For the other platforms, conversions are sent as a whole (though you may configure them in the platform UIs) and cannot be separated by event type in the connector data:

• Amazon Ads
• Apple Search Ads (does not include conversion_value)
• Google Ads
• Microsoft Advertising
• Pinterest Ads
• TikTok Ads

Adding custom metrics to final reports

By default, this package selects clicks, impressions, spend, conversions, and conversions_value metrics from the upstream Ad platform reports. Additionally, each specific upstream Ad platform package allows for custom passthrough metrics to be included in the individual platform's final reports. You can find a complete list of available passthrough metric variables for each platform by referring to the relevant links below and inspecting the additional configurations for each platform: - Amazon Ads - Apple Search Ads - Facebook Ads - Google Ads - LinkedIn Ad Analytics - Microsoft Advertising - Pinterest Ads - Reddit Ads - Snapchat Ads - TikTok Ads - Twitter Ads

Furthermore, this package allows you to include these configured upstream passthrough metrics in the final roll-up models of the combined Ad Reporting package. To include passthrough metrics in the respective final models, you need to define the following ad_reporting__* variables in your dbt_project.yml file:

vars:
  ad_reporting__account_passthrough_metrics:
    - name: view_through_conversions
  ad_reporting__campaign_passthrough_metrics: 
    - name: total_shares
  ad_reporting__ad_group_passthrough_metrics:
    - name: interactions
  ad_reporting__ad_passthrough_metrics: ## For both Ad and URL reports
    - name: video_views_captured
  ad_reporting__keyword_passthrough_metrics:
    - name: interactions
  ad_reporting__search_passthrough_metrics:
    - name: local_spend_amount

It is important to ensure that if you want to configure a passthrough metric for an ad reporting end model, that metric must be included in all of your upstream variables. Additionally, the name of the metric must be consistent across platforms. If a certain upstream platform does not include the metric you must include a transform_sql argument to pass a null value through (see below for examples). The following configuration is an example when using the Microsoft Ads, Apple Search Ads, Google Ads, Snapchat Ads, TikTok Ads, and Reddit Ads platforms within a dbt_project.yml file:

Note: While the below configuration is only for a subset of Ad platforms, the same strategy will be used for all other possible combinations of upstream Ad platform dependencies.

vars:
  ## Account Report Passthrough Metrics
  microsoft_ads__account_passthrough_metrics:
    - name: video_views_captured
      transform_sql: "null"
  apple_search_ads__campaign_passthrough_metrics:
    - name: video_views_captured
      transform_sql: "null"
  google_ads__account_stats_passthrough_metrics:
    - name: video_views
      alias: video_views_captured
      transform_sql: cast(video_views_captured as int64)
  # snapchat_ads__ad_hourly_passthrough_metrics: # Defined below in the ad/url metrics therefore, not needed here but kept for documentation.
  #   - name: video_views
  #     alias: video_views_captured
  #     transform_sql: cast(video_views_captured as int64)
  tiktok_ads__ad_hourly_passthrough_metrics:
    - name: video_watched_2_s
      alias: video_views_captured
      transform_sql: cast(video_views_captured as int64)
  reddit_ads__account_passthrough_metrics: # already includes view_through_conversions
    - name: video_watched_3_seconds
      alias: video_views_captured
      transform_sql: cast(video_views_captured as int64)
  ad_reporting__account_passthrough_metrics:
    - name: video_views_captured

  ## Campaign Report Passthrough Metrics
  microsoft_ads__campaign_passthrough_metrics:
    - name: total_shares
      transform_sql: "null"
  google_ads__campaign_stats_passthrough_metrics:
    - name: total_shares
      transform_sql: cast(total_shares as int)
  snapchat_ads__campaign_hourly_report_passthrough_metrics:
    - name: shares
      alias: total_shares
  tiktok_ads__campaign_hourly_passthrough_metrics:
    - name: shares
      alias: total_shares
  reddit_ads__campaign_passthrough_metrics:
      transform_sql: "null"
    - name: total_shares
      transform_sql: "null"
  ad_reporting__campaign_passthrough_metrics: 
    - name: total_shares

  ## Ad Group Report Passthrough Metrics
  microsoft_ads__ad_group_passthrough_metrics:
    - name: phone_calls
      alias: interactions
  apple_search_ads__ad_group_passthrough_metrics:
    - name: new_downloads
      alias: interactions
  google_ads__ad_group_stats_passthrough_metrics:
    - name: interactions
  snapchat_ads__ad_squad_hourly_passthrough_metrics:
    - name: saves
      alias: interactions
  tiktok_ads__ad_group_hourly_passthrough_metrics:
    - name: likes
      alias: interactions
  reddit_ads__ad_group_passthrough_metrics:
    - name: video_started
      alias: interactions
  ad_reporting__ad_group_passthrough_metrics:
    - name: interactions

## Ad and URL Report Passthrough Metrics
  microsoft_ads__ad_passthrough_metrics:
    - name: video_views_captured
      transform_sql: "null"
  apple_search_ads__ad_passthrough_metrics:
    - name: video_views_captured
      transform_sql: "null"
  google_ads__ad_stats_passthrough_metrics:
    - name: video_views
      alias: video_views_captured
      transform_sql: cast(video_views_captured as int64)
  snapchat_ads__ad_hourly_passthrough_metrics:
    - name: video_views
      alias: video_views_captured
      transform_sql: cast(video_views_captured as int64)
  tiktok_ads__ad_hourly_passthrough_metrics:
    - name: video_watched_2_s
      alias: video_views_captured
      transform_sql: cast(video_views_captured as int64)
  reddit_ads__ad_passthrough_metrics:
    - name: video_watched_3_seconds
      alias: video_views_captured
      transform_sql: cast(video_views_captured as int64)
  ad_reporting__ad_passthrough_metrics:
    - name: video_views_captured

  # Keyword Report Passthrough Metrics
  microsoft_ads__keyword_passthrough_metrics:
    - name: interactions
      transform_sql: "null"
  apple_search_ads__keyword_passthrough_metrics:
    - name: new_downloads
      alias: interactions
  google_ads__keyword_stats_passthrough_metrics:
    - name: interactions
  ad_reporting__keyword_passthrough_metrics:
    - name: interactions

  # Search Report Passthrough Metrics
  microsoft_ads__search_passthrough_metrics:
    - name: local_spend_amount
      transform_sql: "null"
  apple_search_ads__search_term_passthrough_metrics:
    - name: local_spend_amount
      transform_sql: "cast(local_spend_amount as int64)"
      transform_sql: "null"
  ad_reporting__search_passthrough_metrics:
    - name: local_spend_amount

Note: Please make sure to use due diligence when adding metrics to these models. The metrics added by default (clicks, impressions, spend, conversions, and conversions_value) have been vetted by the Fivetran team maintaining this package for accuracy. There are metrics included within the source reports, for example metric averages, which may be inaccurately represented at the grain for reports created in this package. You will want to ensure whichever metrics you pass through are indeed appropriate to aggregate at the respective reporting levels provided in this package.

Disabling null URL filtering from URL reports

The default behavior for the ad_reporting__url_report end model is to filter out records having null URL fields, however, you are able to turn off this filter if needed. To turn off the filter, include the below in your dbt_project.yml file. This variable will affect ALL Fivetran platform packages enabled in Ad Reporting, therefore either all URL reports will have null URLs filtered, or all URL reports will have null URLs included.

vars:
  ad_reporting__url_report__using_null_filter: False # Default is True.

Change the source table references

If an individual source table has a different name than the package expects, add the table name as it appears in your destination to the respective variable. This is not available for sources in which you are unioning together multiple connectors.

IMPORTANT: See the Apple Store dbt_project.yml and Google Play dbt_project.yml variable declarations to see the expected names.

vars:
    <default_source_table_name>_identifier: your_table_name 

(Optional) Step 7: Orchestrate your models with Fivetran Transformations for dbt Core™

Expand for details

Fivetran offers the ability for you to orchestrate your dbt project through Fivetran Transformations for dbt Core™. Learn how to set up your project for orchestration through Fivetran in our Transformations for dbt Core™ setup guides.

(Optional) Step 8: Use predefined Metrics and the dbt Semantic Layer

Expand for details

On top of the ad_reporting__ad_report final model, the Ad Reporting dbt package defines common Metrics using MetricFlow that can be queried with the dbt Semantic Layer. These metrics include:

• Spend
• Impressions
• Clicks
• Cost per click
• Clickthrough rate
• Bounce rate
• Count of active ads
• Average spend
• Average non-zero spend

You can find the supported dimensions and full definitions of these metrics here, and the semantic model definitions here.

Refer to the Semantic Layer quickstart guide for instructions on how to get setup with the dbt Semantic Layer and start querying these metrics.

Metricflow Time Spine Configuration This package includes a model called metricflow_time_spine.sql that MetricFlow requires to build cumulative metrics. Documentation on the metricflow time spine model can be found here. If you have already configured a metricflow time spine model in your project, you will need to disable the one in this package by defining the ad_reporting__metricflow_time_spine_enabled variable as false in your project.

## root dbt_project.yml
vars:
  ad_reporting__metricflow_time_spine_enabled: false ## true by default

Additionally, the dbt_date.get_base_dates macro is used in the generation of the metricsflow_time_spine.sql model. This macro requires the dbt_date:time_zone variable to be defined in the project to generate a time spine based on the defined time zone. The default value in this package is America/Los_Angeles. However, you may override this variable in your own project if you wish.

Note: This variable is defined under the ad_reporting hierarchy within this package and should not adjust any local global variable values in your project if you already have this variable defined. For more information on why this variable is needed and the different value options, refer to the dbt-date package documentation.

## root dbt_project.yml
vars:
  "dbt_date:time_zone": "America/Chicago" # Default is "America/Los_Angeles"

Semantic Manifest You may notice a new run artifact called semantic_manifest.json. This file serves as the integation point between dbt-core and metricflow, and contains all the information MetricFlow needs to build a semantic graph, and generate SQL from query requests. You can learn more about the semantic manifest file in the docs.

Note: Metricflow is only supported in dbt>=v1.6.0, therefore, please take note of the correct dbt version.

Does this package have dependencies?

This dbt package is dependent on the following dbt packages. For more information on the below packages, refer to the dbt hub site.

If you have any of these dependent packages in your own packages.yml I highly recommend you remove them to ensure there are no package version conflicts.

packages: 
  - package: fivetran/fivetran_utils
    version: [">=0.4.0", "<0.5.0"]

  - package: dbt-labs/dbt_utils
    version: [">=0.8.0", "<0.9.0"]

  - package: calogica/dbt_date
    version: [">=0.9.0", "<1.0.0"]

  - package: fivetran/amazon_ads
    version: [">=0.4.0", "<0.5.0"]
  
  - package: fivetran/amazon_ads_source
    version: [">=0.4.0", "<0.5.0"]

  - package: fivetran/apple_search_ads
    version: [">=0.4.0", "<0.5.0"]

  - package: fivetran/apple_search_ads_source
    version: [">=0.4.0", "<0.5.0"]
  
  - package: fivetran/facebook_ads
    version: [">=0.8.0", "<0.9.0"]

  - package: fivetran/facebook_ads_source
    version: [">=0.8.0", "<0.9.0"]
  
  - package: fivetran/google_ads
    version: [">=0.11.0", "<0.12.0"]

  - package: fivetran/google_ads_source
    version: [">=0.11.0", "<0.12.0"]

  - package: fivetran/pinterest
    version: [">=0.11.0", "<0.12.0"]

  - package: fivetran/pinterest_source
    version: [">=0.11.0", "<0.12.0"]

  - package: fivetran/microsoft_ads
    version: [">=0.9.0", "<0.10.0"]

  - package: fivetran/microsoft_ads_source
    version: [">=0.10.0", "<0.11.0"]

  - package: fivetran/linkedin
    version: [">=0.9.0", "<0.10.0"]

  - package: fivetran/linkedin_source
    version: [">=0.9.0", "<0.10.0"]

  - package: fivetran/reddit_ads
    version: [">=0.3.0", "<0.4.0"]

  - package: fivetran/reddit_ads_source
    version: [">=0.3.0", "<0.4.0"]

  - package: fivetran/snapchat_ads
    version: [">=0.7.0", "<0.8.0"]

  - package: fivetran/snapchat_ads_source
    version: [">=0.7.0", "<0.8.0"]

  - package: fivetran/tiktok_ads
    version: [">=0.6.0", "<0.7.0"]

  - package: fivetran/tiktok_ads_source
    version: [">=0.6.0", "<0.7.0"]

  - package: fivetran/twitter_ads
    version: [">=0.8.0", "<0.9.0"]

  - package: fivetran/twitter_ads_source
    version: [">=0.8.0", "<0.9.0"]

How is this package maintained and can I contribute?

Package Maintenance

The Fivetran team maintaining this package only maintains the latest version of the package. We highly recommend you stay consistent with the latest version of the package and refer to the CHANGELOG and release notes for more information on changes across versions.

Opinionated Decisions

In creating this package, which is meant for a wide range of use cases, we had to take opinionated stances on a few different questions we came across during development. We've consolidated significant choices we made in the DECISIONLOG.md, and will continue to update as the package evolves. We are always open to and encourage feedback on these choices, and the package in general.

Contributions

A small team of analytics engineers at Fivetran develops these dbt packages. However, the packages are made better by community contributions.

We highly encourage and welcome contributions to this package. Check out this dbt Discourse article on the best workflow for contributing to a package.

Contributors

We thank everyone who has taken the time to contribute. Each PR, bug report, and feature request has made this package better and is truly appreciated.

A special thank you to Seer Interactive, who we closely collaborated with to introduce native conversion support to our Ad packages.

Are there any resources available?

• If you encounter any questions or want to reach out for help, see the GitHub Issue section to find the right avenue of support for you.
• If you would like to provide feedback to the dbt package team at Fivetran, or would like to request a future dbt package to be developed, then feel free to fill out our Feedback Form.